In [1]:
import os

## Set directory
os.chdir('/hpc/group/pbenfeylab/CheWei/CW_data/genesys')

import networkx as nx
from genesys_evaluate_v1 import *
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import warnings
# Suppress all warning messages
warnings.filterwarnings("ignore", category=DeprecationWarning)

/hpc/group/pbenfeylab/ch416/miniconda3/envs/genesys/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
In [2]:
## Conda Env genesys on DCC
print(torch.__version__)
print(sc.__version__) 

1.11.0
1.9.6
In [3]:
## Genes considered/used (shared among samples) 
gene_list = pd.read_csv('./gene_list_1108.csv')

Load Data¶

In [4]:
with open("./genesys_root_data.pkl", 'rb') as file_handle:
    data = pickle.load(file_handle)
    
batch_size = 2000
#dataset = Root_Dataset(data['X_test'], data['y_test'])
#loader = DataLoader(dataset,
#                         batch_size = batch_size,
#                         shuffle = True, drop_last=True)

X_all = np.vstack((data['X_train'],data['X_val'],data['X_test']))
y_all = pd.concat((data['y_train'],data['y_val'],data['y_test']))
dataset = Root_Dataset(X_all, y_all)
loader = DataLoader(dataset,
                         batch_size = batch_size,
                         shuffle = True, drop_last=True)
In [5]:
classes = ['Columella', 'Lateral Root Cap', 'Phloem', 'Xylem', 'Procambium', 'Pericycle', 'Endodermis', 'Cortex', 'Atrichoblast', 'Trichoblast']
class2num = {c: i for (i, c) in enumerate(classes)}
num2class = {i: c for (i, c) in enumerate(classes)}
In [6]:
cts = ['Atrichoblast','Trichoblast','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Lateral Root Cap','Columella']
ctw = np.zeros((len(cts), 17513, 17513))
## number of cells sampled from the atlas
batch_size = 2000

Extract a sample¶

In [7]:
sample = next(iter(loader))
xo = sample['x'].to(device)
y = sample['y'].to(device)
y_label = [num2class[i] for i in y.tolist()]
In [8]:
xo.shape
Out[8]:
torch.Size([2000, 11, 17513])
In [9]:
len(y_label)
Out[9]:
2000
In [10]:
len(loader)
Out[10]:
55
In [11]:
## GRN for the transition t3 to t5
for ct in cts:
    print(ct)
    cws = np.zeros((len(loader), 17513, 17513))
    with torch.no_grad():
        for i, sample in enumerate(loader):
            x = sample['x'].to(device)
            y = sample['y'].to(device)
            y_label = [num2class[i] for i in y.tolist()]
            
            #pred_h = model.init_hidden(batch_size)
            #tfrom = model.generate_current(x, pred_h, 0).to('cpu').detach().numpy()
            #cfrom = tfrom[np.where(np.array(y_label)==ct)[0],:]
            cfrom = x[np.where(np.array(y_label)==ct)[0],3,:] 
            
            #pred_h = model.init_hidden(batch_size)
            #tto = model.generate_next(x, pred_h, 0).to('cpu').detach().numpy()   
            #cto = tto[np.where(np.array(y_label)==ct)[0],:]
            cto = x[np.where(np.array(y_label)==ct)[0],5,:]
            
            cw = torch.linalg.lstsq(torch.tensor(cfrom), torch.tensor(cto)).solution.detach().numpy()
            cws[i] = cw
    
    ## Calculate mean across number of repeats
    cwm = np.mean(cws, axis=0)
    ctw[cts.index(ct)] = cwm

Atrichoblast

/tmp/ipykernel_880125/1395650234.py:21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  cw = torch.linalg.lstsq(torch.tensor(cfrom), torch.tensor(cto)).solution.detach().numpy()

Trichoblast
Cortex
Endodermis
Pericycle
Procambium
Xylem
Phloem
Lateral Root Cap
Columella
In [12]:
# Save the array to disk
np.save('genesys_raw_ctw_t3-t5.npy', ctw)
In [11]:
ctw = np.load('genesys_raw_ctw_t3-t5.npy')
In [12]:
## Calculate z-scores
ctw_z = np.zeros((len(cts), 17513, 17513))
for i in range(len(cts)):
    if np.std(ctw[i])==0:
        ctw_z[i] = np.zeros((17513, 17513))
    else:
        ctw_z[i] = (ctw[i] - np.mean(ctw[i])) / np.std(ctw[i])
In [13]:
## Filtering based on z-scores (with no weights)
ctw_f = np.zeros((len(cts), 17513, 17513))
## z-score threshold (keep values > mean + threshold*std)
threshold=3
for i in range(len(cts)):
    ctw_f[i] = np.abs(ctw_z[i]) > threshold

Load TFs list¶

In [14]:
wanted_TFs = pd.read_csv("./Kay_TF_thalemine_annotations.csv")
In [15]:
## Make TF names unique and assign preferred names
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G33880"]="WOX9"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G45160"]="SCL27"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G04410"]="NAC78"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G29035"]="ORS1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02540"]="ZHD3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G16500"]="IAA26"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G09740"]="HAG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G24660"]="ZHD2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G46880"]="HDG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G28420"]="RLT1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G14580"]="BLJ"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G45260"]="BIB"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02070"]="RVN"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G28160"]="FIT"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G68360"]="GIS3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G20640"]="NLP4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G05550"]="VFP5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G59470"]="FRF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G15150"]="HAT7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G14750"]="WER"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G75710"]="BRON"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G74500"]="TMO7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G12646"]="RITF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G48100"]="ARR5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G16141"]="GATA17L"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G65640"]="NFL"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G62700"]="VND5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G36160"]="VND2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G66300"]="VND3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G12260"]="VND4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G62380"]="VND6"
In [16]:
pd.Series(wanted_TFs['Name']).value_counts().head(5)
Out[16]:
Name
NAC001    1
PRE5      1
MYB118    1
MYB21     1
MYB0      1
Name: count, dtype: int64

Network Analysis¶

In [17]:
TFidx = []
for i in wanted_TFs['GeneID']:
    if i in gene_list['features'].tolist():
        TFidx.append(np.where(gene_list['features']==i)[0][0])

TFidx = np.sort(np.array(TFidx))
In [18]:
def network(i):
    ## No weights
    adj_nw = ctw_f[i]
    ## Weighted
    adj = ctw[i]*ctw_f[i]
    ## TF only
    adj = adj[np.ix_(TFidx,TFidx)]
    adj_nw = adj_nw[np.ix_(TFidx,TFidx)]
    
    ## Remove no connect 
    regidx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[0]).value_counts().index[pd.Series(np.where(adj_nw==True)[0]).value_counts()>=1]))
    taridx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[1]).value_counts().index[pd.Series(np.where(adj_nw==True)[1]).value_counts()>=1]))
    ## Reciprocol
    keepidx = np.sort(np.array(list(set(regidx).intersection(taridx))))
    #keepidx = np.sort(np.array(list(set(regidx).union(taridx))))
    
    TFID = np.array(gene_list['features'][TFidx])[keepidx].tolist()
    ## TF name to keep
    TFname = []
    for i in np.array(gene_list['features'][TFidx])[keepidx]:
        TFname.append(wanted_TFs['Name'][np.where(wanted_TFs['GeneID']==i)[0][0]])
        
    adj = adj[np.ix_(keepidx,keepidx)]
    
    # Create a NetworkX graph for non-directed edges
    G = nx.Graph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(j, i, weight=abs(weight), distance=1/abs(weight))
            

    ## Measures the extent to which how close a node is to all other nodes in the network, considering the shortest paths or geodesic distances between nodes
    closeness_centrality = nx.closeness_centrality(G, distance='distance')
    ## Measures the extent to which a node that are not only well-connected but also connected to other well-connected nodes.
    eigenvector_centrality = nx.eigenvector_centrality(G)
    
    # Create a NetworkX graph for diected edges
    G = nx.MultiDiGraph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(j, i, weight=weight)
    
    ## Measures the number of connections (edges) each node has
    degree_centrality = nx.degree_centrality(G)
    # Calculate outgoing centrality
    out_centrality = nx.out_degree_centrality(G)
    # Calculate incoming centrality
    in_centrality = nx.in_degree_centrality(G)
    ## Measures the extent to which a node lies on the shortest paths between other nodes.
    betweenness_centrality = nx.betweenness_centrality(G, weight='weight')
    
    ## Non_Reciprocal Out centrality
    # Visualize the graph
    pos = nx.spring_layout(G)  # Positions of the nodes
    
    # Node colors based on weighted betweenness centrality
    node_colors = [out_centrality[node] for node in G.nodes()]
    
    # Node sizes based on weighted betweenness centrality
    node_sizes = [out_centrality[node] * 1000 for node in G.nodes()]

    # Get the edge weights as a dictionary
    edge_weights = nx.get_edge_attributes(G, 'weight')
    edge_colors = ['red' if weight > 0 else 'blue' for (_, _, weight) in G.edges(data='weight')]
    
    # Scale the edge weights to desired linewidths
    max_weight = max(edge_weights.values())
    edge_widths = [float(edge_weights[edge]) / max_weight for edge in G.edges]
    
    # Draw the graph
    nx.draw(G, pos=pos, node_color=node_colors, node_size=node_sizes, with_labels=False, width=edge_widths, edge_color=edge_colors)
    # Add node labels
    labels = {node: G.nodes[node]['name'] for node in G.nodes}
    nx.draw_networkx_labels(G, pos=pos, labels=labels, font_size=8)
    
    # Add a colorbar to show the weighted betweenness centrality color mapping
    sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=min(node_colors), vmax=max(node_colors)))
    sm.set_array([])
    plt.colorbar(sm)
    
    # Show the plot
    plt.show()
    
    dc = pd.DataFrame.from_dict(degree_centrality, orient='index', columns=['degree_centrality'])
    oc = pd.DataFrame.from_dict(out_centrality, orient='index', columns=['out_centrality'])
    ic = pd.DataFrame.from_dict(in_centrality, orient='index', columns=['in_centrality'])
    bc = pd.DataFrame.from_dict(betweenness_centrality, orient='index', columns=['betweenness_centrality'])
    cc = pd.DataFrame.from_dict(closeness_centrality, orient='index', columns=['closeness_centrality'])
    ec = pd.DataFrame.from_dict(eigenvector_centrality, orient='index', columns=['eigenvector_centrality'])
    df = pd.concat([dc,oc,ic,bc,cc,ec], axis=1)
    df.index =TFname
    df = df.sort_values('betweenness_centrality', ascending=False)
    
    return(df)
In [19]:
atri = network(0)
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In [20]:
atri
Out[20]:
degree_centrality out_centrality in_centrality betweenness_centrality closeness_centrality eigenvector_centrality
GL2 0.621374 0.337405 0.283969 0.822793 0.000220 0.154203
KDR 0.374046 0.045802 0.328244 0.782361 0.000215 0.128245
PHE1 0.134351 0.122137 0.012214 0.772895 0.000187 0.067567
IAA1 0.114504 0.042748 0.071756 0.728954 0.000169 0.058109
AT4G31650 0.285496 0.145038 0.140458 0.689047 0.000188 0.110174
... ... ... ... ... ... ...
FRS10 0.007634 0.001527 0.006107 0.000000 0.000117 0.007585
BZO2H3 0.007634 0.006107 0.001527 0.000000 0.000105 0.007373
LUG 0.009160 0.000000 0.009160 0.000000 0.000127 0.012093
PIE1 0.007634 0.003053 0.004580 0.000000 0.000130 0.009628
MBF1A 0.006107 0.003053 0.003053 0.000000 0.000122 0.008376

656 rows × 6 columns

In [21]:
tri = network(1)
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In [22]:
cor = network(2)
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In [23]:
end = network(3)
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In [24]:
per = network(4)
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In [25]:
pro = network(5)
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In [26]:
xyl = network(6)
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In [27]:
phl = network(7)
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In [28]:
lrc = network(8)
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In [29]:
col = network(9)
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In [30]:
atri.columns = ['atri_degree_centrality','atri_out_centrality','atri_in_centrality','atri_betweenness_centrality','atri_closeness_centrality','atri_eigenvector_centrality']
tri.columns = ['tri_degree_centrality','tri_out_centrality','tri_in_centrality','tri_betweenness_centrality','tri_closeness_centrality','tri_eigenvector_centrality']
cor.columns = ['cor_degree_centrality','cor_out_centrality','cor_in_centrality','cor_betweenness_centrality','cor_closeness_centrality','cor_eigenvector_centrality']
end.columns = ['end_degree_centrality','end_out_centrality','end_in_centrality','end_betweenness_centrality','end_closeness_centrality','end_eigenvector_centrality']
per.columns = ['per_degree_centrality','per_out_centrality','per_in_centrality','per_betweenness_centrality','per_closeness_centrality','per_eigenvector_centrality']
pro.columns = ['pro_degree_centrality','pro_out_centrality','pro_in_centrality','pro_betweenness_centrality','pro_closeness_centrality','pro_eigenvector_centrality']
xyl.columns = ['xyl_degree_centrality','xyl_out_centrality','xyl_in_centrality','xyl_betweenness_centrality','xyl_closeness_centrality','xyl_eigenvector_centrality']
phl.columns = ['phl_degree_centrality','phl_out_centrality','phl_in_centrality','phl_betweenness_centrality','phl_closeness_centrality','phl_eigenvector_centrality']
lrc.columns = ['lrc_degree_centrality','lrc_out_centrality','lrc_in_centrality','lrc_betweenness_centrality','lrc_closeness_centrality','lrc_eigenvector_centrality']
col.columns = ['col_degree_centrality','col_out_centrality','col_in_centrality','col_betweenness_centrality','col_closeness_centrality','col_eigenvector_centrality']
In [31]:
## Indentify main regulators in each net work
tff = []
tff = tff + atri[atri['atri_betweenness_centrality']>0].index.tolist()
tff = tff + tri[tri['tri_betweenness_centrality']>0].index.tolist()
tff = tff + lrc[lrc['lrc_betweenness_centrality']>0].index.tolist()
tff = tff + cor[cor['cor_betweenness_centrality']>0].index.tolist()
tff = tff + end[end['end_betweenness_centrality']>0].index.tolist()
tff = tff + per[per['per_betweenness_centrality']>0].index.tolist()
tff = tff + pro[pro['pro_betweenness_centrality']>0].index.tolist()
tff = tff + xyl[xyl['xyl_betweenness_centrality']>0].index.tolist()
tff = tff + phl[phl['phl_betweenness_centrality']>0].index.tolist()
tff = tff + col[col['col_betweenness_centrality']>0].index.tolist()
tf_occurance = pd.DataFrame(pd.Series(tff).value_counts())
tf_occurance = tf_occurance.rename(columns={
    'count': 'tf_occurance'
})
tf_spec = pd.concat([tf_occurance, atri, tri, lrc, cor, end, per, pro, xyl, phl, col], axis=1)
tf_spec = tf_spec.fillna(0)
In [32]:
## Epidermis (atri, tri, lrc)
celltype1='atri'
celltype2='tri'
celltype3='lrc'
ts = tf_spec[tf_spec['tf_occurance']==3][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==9].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[32]:
atri_betweenness_centrality tri_betweenness_centrality lrc_betweenness_centrality atri_out_centrality tri_out_centrality lrc_out_centrality atri_in_centrality tri_in_centrality lrc_in_centrality centrality_count centrality_sum
ATS 0.007573 0.006909 0.075989 0.047328 0.00969 0.142070 0.412214 0.160853 0.264317 9 10.126943
RHC2A 0.010750 0.000143 0.000010 0.007634 0.00969 0.052863 0.021374 0.027132 0.024229 9 9.153825
In [33]:
## atri, tri
celltype1='atri'
celltype2='tri'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[33]:
atri_betweenness_centrality tri_betweenness_centrality atri_out_centrality tri_out_centrality atri_in_centrality tri_in_centrality centrality_count centrality_sum
WRKY65 0.001515 0.126620 0.151145 0.579457 0.087023 0.393411 6 7.339171
PHE1 0.772895 0.001099 0.122137 0.025194 0.012214 0.003876 6 6.937415
AT2G37120 0.008119 0.022876 0.146565 0.503876 0.109924 0.114341 6 6.905700
ERF104 0.011700 0.132242 0.091603 0.050388 0.074809 0.038760 6 6.399502
MYB45 0.000329 0.000008 0.280916 0.015504 0.009160 0.005814 6 6.311731
CRF4 0.005862 0.000045 0.036641 0.013566 0.181679 0.050388 6 6.288181
WRKY61 0.073308 0.020392 0.030534 0.129845 0.009160 0.021318 6 6.284558
WRKY45 0.008525 0.015060 0.134351 0.069767 0.010687 0.003876 6 6.242267
GL3 0.001501 0.000715 0.019847 0.096899 0.007634 0.015504 6 6.142100
WRKY42 0.000423 0.000162 0.018321 0.034884 0.010687 0.054264 6 6.118739
KAPP 0.001503 0.000188 0.007634 0.034884 0.007634 0.019380 6 6.071222
AT2G38950 0.000124 0.000004 0.004580 0.009690 0.018321 0.021318 6 6.054036
In [34]:
## Atrichoblast specific
celltype = 'atri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[34]:
atri_betweenness_centrality atri_out_centrality atri_in_centrality centrality_count centrality_sum
GL2 0.822793 0.337405 0.283969 3 4.444167
AT4G31650 0.689047 0.145038 0.140458 3 3.974543
TRY 0.625422 0.082443 0.114504 3 3.822369
MYB47 0.433536 0.018321 0.010687 3 3.462544
BNQ3 0.424570 0.090076 0.140458 3 3.655104
OFP18 0.399559 0.215267 0.082443 3 3.697269
MEA 0.079443 0.279389 0.030534 3 3.389367
TTG2 0.042475 0.427481 0.213740 3 3.683696
AT2G28710 0.029131 0.215267 0.013740 3 3.258139
NAC6 0.006700 0.212214 0.003053 3 3.221967
AT1G11490 0.005098 0.007634 0.067176 3 3.079908
AT3G05860 0.002015 0.216794 0.003053 3 3.221862
AT2G25650 0.001919 0.003053 0.009160 3 3.014133
AT5G06420 0.001849 0.006107 0.012214 3 3.020169
GIS3 0.001629 0.003053 0.056489 3 3.061171
EIL2 0.001503 0.004580 0.003053 3 3.009137
ATMAK3 0.001501 0.016794 0.058015 3 3.076310
AGL102 0.001424 0.010687 0.032061 3 3.044172
AT2G18670 0.001319 0.068702 0.003053 3 3.073075
AL4 0.000381 0.004580 0.010687 3 3.015648
TCP24 0.000222 0.004580 0.041221 3 3.046023
AT1G18960 0.000028 0.010687 0.004580 3 3.015295
WRKY13 0.000019 0.018321 0.001527 3 3.019866
In [35]:
## Trichoblast specific
celltype = 'tri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[35]:
tri_betweenness_centrality tri_out_centrality tri_in_centrality centrality_count centrality_sum
RSL4 0.400903 0.684109 0.042636 3 4.127647
AT4G09100 0.391574 0.759690 0.019380 3 4.170644
RHD6 0.298728 0.494186 0.230620 3 4.023534
LRL3 0.183950 0.662791 0.032946 3 3.879687
AT3G53370 0.153357 0.827519 0.358527 3 4.339403
RSL1 0.120291 0.015504 0.021318 3 3.157112
SUVR4 0.108617 0.038760 0.071705 3 3.219083
ATMYC1 0.086987 0.137597 0.147287 3 3.371871
EGL3 0.045590 0.011628 0.048450 3 3.105667
AT5G04390 0.015350 0.060078 0.003876 3 3.079303
BEH1 0.013698 0.017442 0.003876 3 3.035015
AT2G05160 0.004847 0.238372 0.025194 3 3.268413
AT3G09735 0.004094 0.114341 0.085271 3 3.203707
RL6 0.003278 0.089147 0.032946 3 3.125371
AT1G04850 0.002788 0.021318 0.108527 3 3.132633
NPH4 0.002559 0.021318 0.034884 3 3.058760
AT2G01060 0.002311 0.034884 0.005814 3 3.043008
ABF3 0.001923 0.023256 0.063953 3 3.089132
AT5G58900 0.001057 0.013566 0.021318 3 3.035941
HSFA1D 0.000497 0.013566 0.019380 3 3.033442
bZIP2 0.000482 0.081395 0.153101 3 3.234978
LOL2 0.000448 0.038760 0.015504 3 3.054711
AT2G36480 0.000395 0.015504 0.011628 3 3.027527
MED6 0.000357 0.021318 0.023256 3 3.044931
RAP2.2 0.000354 0.032946 0.023256 3 3.056555
AT1G01920 0.000339 0.017442 0.036822 3 3.054602
TAFII15 0.000331 0.032946 0.032946 3 3.066223
AT3G05760 0.000320 0.042636 0.029070 3 3.072025
AT5G13780 0.000218 0.013566 0.058140 3 3.071924
WRKY3 0.000199 0.063953 0.034884 3 3.099037
TRB3 0.000177 0.009690 0.021318 3 3.031185
NAGS1 0.000124 0.054264 0.017442 3 3.071830
HFR1 0.000124 0.058140 0.005814 3 3.064078
NAC027 0.000113 0.015504 0.005814 3 3.021431
FRS8 0.000071 0.001938 0.009690 3 3.011699
LBD23 0.000068 0.005814 0.005814 3 3.011696
AT5G47660 0.000068 0.007752 0.005814 3 3.013634
BPC5 0.000041 0.011628 0.042636 3 3.054305
GATA27 0.000038 0.038760 0.001938 3 3.040735
AT3G04930 0.000023 0.009690 0.050388 3 3.060100
BPC2 0.000023 0.005814 0.027132 3 3.032968
AT5G03500 0.000023 0.060078 0.013566 3 3.073666
GBF3 0.000019 0.011628 0.013566 3 3.025213
NFXL2 0.000008 0.023256 0.034884 3 3.058147
AT3G24820 0.000008 0.040698 0.019380 3 3.060085
ZFN3 0.000004 0.013566 0.032946 3 3.046515
RAP2.11 0.000004 0.011628 0.013566 3 3.025198
RAP74 0.000004 0.007752 0.034884 3 3.042639
AT2G24830 0.000004 0.009690 0.029070 3 3.038763
In [36]:
## LRC specific
celltype = 'lrc'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[36]:
lrc_betweenness_centrality lrc_out_centrality lrc_in_centrality centrality_count centrality_sum
CRF11 0.716023 0.015419 0.047357 3 3.778798
E2F3 0.556675 0.038546 0.101322 3 3.696543
ARF8 0.550293 0.034141 0.160793 3 3.745227
AT2G36026 0.516237 0.061674 0.048458 3 3.626369
AT3G55080 0.506070 0.008811 0.012115 3 3.526995
... ... ... ... ... ...
AT1G19000 0.000001 0.056167 0.042952 3 3.099120
SPL13A 0.000001 0.015419 0.001101 3 3.016521
AT3G24490 0.000001 0.006608 0.004405 3 3.011014
HDG2 0.000001 0.037445 0.079295 3 3.116741
AT2G48100 0.000001 0.037445 0.023128 3 3.060574

72 rows × 5 columns

In [37]:
## Columella specific
celltype = 'col'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[37]:
col_betweenness_centrality col_out_centrality col_in_centrality centrality_count centrality_sum
AT5G03220 0.355417 0.005229 0.035294 3 3.395940
NAC016 0.143016 0.011765 0.111111 3 3.265892
HSFA8 0.132959 0.024837 0.043137 3 3.200932
NAM 0.080385 0.194771 0.016993 3 3.292150
NAC069 0.058469 0.049673 0.027451 3 3.135594
... ... ... ... ... ...
AT4G08455 0.000003 0.009150 0.006536 3 3.015690
ASIL2 0.000003 0.019608 0.026144 3 3.045755
SUVH6 0.000002 0.047059 0.036601 3 3.083662
AT3G20280 0.000002 0.006536 0.018301 3 3.024838
AL5 0.000002 0.003922 0.030065 3 3.033989

76 rows × 5 columns

In [38]:
## Ground tissue
celltype1='cor'
celltype2='end'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[38]:
cor_betweenness_centrality end_betweenness_centrality cor_out_centrality end_out_centrality cor_in_centrality end_in_centrality centrality_count centrality_sum
AT4G30180 0.905383 0.990621 0.028881 0.046256 0.126354 0.092511 6 8.190006
RVN 0.603378 0.903634 0.050542 0.018722 0.092058 0.035242 6 7.703575
JKD 0.041020 0.298130 0.422383 0.285242 0.310469 0.225771 6 7.583016
AT3G24120 0.001430 0.985591 0.088448 0.174009 0.028881 0.127753 6 7.406111
ERF15 0.001808 0.907057 0.034296 0.135463 0.045126 0.070485 6 7.194235
BLJ 0.008275 0.088271 0.019856 0.642070 0.046931 0.270925 6 7.076328
BIB 0.001727 0.698725 0.009025 0.162996 0.018051 0.146476 6 7.036999
AT3G61420 0.001289 0.001372 0.027076 0.093612 0.028881 0.051762 6 6.203993
AT1G05710 0.006362 0.000427 0.034296 0.026432 0.070397 0.044053 6 6.181967
AT1G64380 0.000659 0.001137 0.036101 0.027533 0.016245 0.039648 6 6.121323
MCC1 0.001521 0.004245 0.009025 0.004405 0.041516 0.007709 6 6.068422
NST1 0.001550 0.000648 0.009025 0.006608 0.003610 0.001101 6 6.022543
In [39]:
## Cortex specific
celltype = 'cor'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[39]:
cor_betweenness_centrality cor_out_centrality cor_in_centrality centrality_count centrality_sum
AT2G42660 0.941419 0.231047 0.296029 3 4.468495
SIGF 0.800455 0.084838 0.041516 3 3.926809
AT2G38300 0.657255 0.463899 0.272563 3 4.393717
IDD4 0.271179 0.090253 0.120939 3 3.482371
NAC78 0.178240 0.019856 0.021661 3 3.219756
RGL3 0.084599 0.361011 0.153430 3 3.599040
WRKY50 0.037041 0.025271 0.003610 3 3.065922
BZR1 0.009339 0.113718 0.171480 3 3.294537
bZIP52 0.008875 0.092058 0.037906 3 3.138839
CLF 0.007031 0.005415 0.021661 3 3.034107
AT2G46810 0.003649 0.028881 0.077617 3 3.110147
EIL1 0.002905 0.088448 0.074007 3 3.165360
AT1G77800 0.001100 0.014440 0.003610 3 3.019151
MYB4R1 0.000264 0.009025 0.005415 3 3.014705
AT5G12850 0.000157 0.048736 0.068592 3 3.117485
PIL5 0.000140 0.018051 0.005415 3 3.023606
AL1 0.000069 0.028881 0.066787 3 3.095736
COL4 0.000042 0.046931 0.095668 3 3.142642
AT3G10760 0.000016 0.025271 0.018051 3 3.043338
AT5G08430 0.000010 0.009025 0.001805 3 3.010840
AT4G22360 0.000003 0.005415 0.018051 3 3.023469
In [40]:
## Endodermis specific
celltype = 'end'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[40]:
end_betweenness_centrality end_out_centrality end_in_centrality centrality_count centrality_sum
AT5G41920 0.989945 0.109031 0.034141 3 4.133117
HRS1 0.981604 0.107930 0.024229 3 4.113763
SCR 0.920225 0.231278 0.255507 3 4.407009
MYB68 0.693402 0.725771 0.438326 3 4.857498
RAX2 0.566302 0.640969 0.180617 3 4.387887
AT4G19985 0.382847 0.005507 0.013216 3 3.401570
AT3G07500 0.344444 0.008811 0.023128 3 3.376382
AT5G59450 0.327179 0.248899 0.044053 3 3.620130
chr31 0.301218 0.247797 0.189427 3 3.738443
TIFY8 0.210941 0.003304 0.001101 3 3.215347
TCP9 0.138930 0.025330 0.011013 3 3.175274
AT4G00390 0.121904 0.012115 0.019824 3 3.153843
NF-YC12 0.010596 0.002203 0.006608 3 3.019406
AT2G10950 0.004510 0.013216 0.070485 3 3.088210
ERF10 0.001353 0.077093 0.058370 3 3.136815
WLIM2b 0.001218 0.068282 0.096916 3 3.166416
WRKY47 0.001101 0.004405 0.034141 3 3.039648
AT4G36860 0.000892 0.063877 0.048458 3 3.113227
AT1G58110 0.000646 0.029736 0.022026 3 3.052408
TTR1 0.000407 0.002203 0.039648 3 3.042257
AT1G14600 0.000210 0.004405 0.003304 3 3.007919
JAZ12 0.000210 0.052863 0.036344 3 3.089417
SIGA 0.000078 0.011013 0.031938 3 3.043029
AT1G61960 0.000058 0.001101 0.002203 3 3.003362
ULT2 0.000053 0.022026 0.014317 3 3.036397
AT5G42700 0.000039 0.004405 0.026432 3 3.030876
EBS 0.000036 0.025330 0.029736 3 3.055103
ABF2 0.000033 0.020925 0.067181 3 3.088139
AGL16 0.000023 0.073789 0.035242 3 3.109054
AT1G24610 0.000015 0.005507 0.045154 3 3.050675
LCL1 0.000004 0.045154 0.064978 3 3.110136
LAF1 0.000001 0.018722 0.049559 3 3.068283
In [41]:
## Stele
celltype1='per'
celltype2='pro'
celltype3='xyl'
celltype4='phl'
ts = tf_spec[tf_spec['tf_occurance']==4][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype4+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype4+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality', celltype4+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==12].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[41]:
per_betweenness_centrality pro_betweenness_centrality xyl_betweenness_centrality phl_betweenness_centrality per_out_centrality pro_out_centrality xyl_out_centrality phl_out_centrality per_in_centrality pro_in_centrality xyl_in_centrality phl_in_centrality centrality_count centrality_sum
AT3G43430 0.752444 0.369858 0.712880 0.508657 0.625749 0.798768 0.071338 0.504399 0.717565 0.523614 0.072611 0.498534 12 18.156416
MYB20 0.123957 0.903478 0.003828 0.964168 0.319361 0.750513 0.087898 0.030303 0.624750 0.450719 0.202548 0.072336 12 16.533860
AT5G50010 0.000006 0.936064 0.509101 0.140984 0.048902 0.519507 0.110828 0.030303 0.118762 0.099589 0.086624 0.091887 12 14.692558
IAA11 0.941962 0.045362 0.003760 0.432687 0.213573 0.148871 0.142675 0.254154 0.120758 0.052361 0.132484 0.088954 12 14.577603
IAA13 0.657051 0.080992 0.000265 0.017721 0.148703 0.396304 0.087898 0.286413 0.083832 0.214579 0.131210 0.168133 12 14.273099
REV 0.146146 0.023788 0.988311 0.020703 0.013972 0.376797 0.095541 0.055718 0.010978 0.245380 0.107006 0.056696 12 14.141037
TCP14 0.000057 0.439130 0.000042 0.327144 0.041916 0.632444 0.034395 0.183773 0.033932 0.178645 0.112102 0.157380 12 14.140960
AT3G20640 0.008795 0.243349 0.273070 0.969490 0.071856 0.157084 0.038217 0.121212 0.057884 0.096509 0.005096 0.065494 12 14.108055
AT5G09460 0.095466 0.000981 0.631233 0.953595 0.012974 0.055441 0.108280 0.057674 0.008982 0.035934 0.043312 0.077224 12 14.081097
AT1G69580 0.006988 0.286851 0.079207 0.234469 0.109780 0.277207 0.081529 0.347996 0.050898 0.198152 0.048408 0.243402 12 13.964887
IDD16 0.000708 0.356804 0.024495 0.958786 0.008982 0.020534 0.008917 0.064516 0.014970 0.013347 0.058599 0.084066 12 13.614724
WOX13 0.092204 0.000130 0.004967 0.416948 0.223553 0.048255 0.160510 0.057674 0.232535 0.155031 0.140127 0.054741 12 13.586674
AT1G64620 0.974197 0.000085 0.003614 0.144865 0.078842 0.007187 0.068790 0.038123 0.127745 0.019507 0.050955 0.024438 12 13.538349
AT2G29660 0.130516 0.067628 0.002662 0.008869 0.042914 0.366530 0.011465 0.110459 0.042914 0.106776 0.174522 0.153470 12 13.218726
AT3G50650 0.005984 0.015628 0.070238 0.234946 0.036926 0.060575 0.002548 0.018573 0.047904 0.078029 0.010191 0.048876 12 12.630417
In [42]:
## Pericycle
celltype = 'per'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[42]:
per_betweenness_centrality per_out_centrality per_in_centrality centrality_count centrality_sum
AT4G00940 9.827667e-01 0.012974 0.014970 3 4.010711
AT3G14740 9.487768e-01 0.005988 0.023952 3 3.978717
GATA23 9.382613e-01 0.064870 0.116766 3 4.119898
bZIP4 9.355525e-01 0.012974 0.026946 3 3.975473
LBD14 9.335814e-01 0.110778 0.178643 3 4.223003
SIG2 7.194173e-01 0.012974 0.019960 3 3.752351
bZIP58 6.535271e-01 0.013972 0.103792 3 3.771292
AT1G61990 3.060323e-01 0.004990 0.005988 3 3.317010
AT4G20970 2.269008e-01 0.021956 0.042914 3 3.291771
MGP 1.723436e-01 0.092814 0.100798 3 3.365956
AT3G56570 1.653137e-01 0.004990 0.004990 3 3.175294
AT4G27240 1.582469e-01 0.007984 0.035928 3 3.202159
AT5G38840 1.383118e-01 0.015968 0.015968 3 3.170248
LAS 1.247944e-01 0.082834 0.028942 3 3.236571
WRKY67 9.946441e-02 0.026946 0.021956 3 3.148367
IDD11 9.536472e-02 0.151697 0.129741 3 3.376802
SPL10 8.376753e-02 0.001996 0.000998 3 3.086762
bHLH11 4.252534e-02 0.023952 0.006986 3 3.073463
AT5G63080 4.190420e-02 0.001996 0.002994 3 3.046894
ABAP1 3.113354e-02 0.001996 0.015968 3 3.049098
HDA3 2.883544e-02 0.090818 0.143713 3 3.263366
EMB3114 2.773374e-02 0.017964 0.021956 3 3.067654
WHY3 2.630902e-02 0.008982 0.008982 3 3.044273
GATA7 2.094811e-02 0.010978 0.015968 3 3.047894
AT2G14880 1.657125e-02 0.083832 0.105788 3 3.206192
RSZ22a 1.335192e-02 0.014970 0.021956 3 3.050278
AT1G10320 1.279858e-02 0.005988 0.008982 3 3.027769
BBX18 1.265800e-02 0.012974 0.036926 3 3.062558
AT1G76510 1.161214e-02 0.009980 0.017964 3 3.039556
AT3G21330 8.982036e-03 0.005988 0.056886 3 3.071856
DEL3 3.279156e-03 0.000998 0.011976 3 3.016253
AT1G03650 3.090722e-03 0.014970 0.023952 3 3.042013
tny 2.588230e-03 0.004990 0.007984 3 3.015562
BOP2 2.480553e-03 0.016966 0.046906 3 3.066353
TSO1 1.052839e-03 0.020958 0.012974 3 3.034985
AT3G11450 9.980040e-04 0.020958 0.043912 3 3.065868
AT1G79220 9.980040e-04 0.029940 0.002994 3 3.033932
DUO1 9.980040e-04 0.005988 0.009980 3 3.016966
ATE2F2 9.970070e-04 0.005988 0.017964 3 3.024949
AT2G42040 9.830489e-04 0.054890 0.020958 3 3.076831
EMB2773 6.311054e-04 0.003992 0.002994 3 3.007617
AT2G34450 5.044855e-04 0.010978 0.053892 3 3.065375
AT4G25210 3.978058e-04 0.022954 0.049900 3 3.073252
LBD25 2.671979e-04 0.054890 0.006986 3 3.062143
AT4G11680 2.562308e-04 0.014970 0.015968 3 3.031194
SPL7 2.123625e-04 0.011976 0.011976 3 3.024164
AT1G24210 1.455630e-04 0.012974 0.020958 3 3.034078
BIM3 1.156528e-04 0.027944 0.013972 3 3.042032
FRS11 1.126618e-04 0.001996 0.010978 3 3.013087
AT5G05130 1.016947e-04 0.012974 0.026946 3 3.040022
TEM1 9.371866e-05 0.072854 0.030938 3 3.103886
MBD13 8.574260e-05 0.027944 0.012974 3 3.041004
NF-YB10 7.278151e-05 0.017964 0.025948 3 3.043985
AT2G04740 5.184436e-05 0.009980 0.027944 3 3.037976
CRF12 4.785634e-05 0.006986 0.026946 3 3.033980
AT4G26810 9.970070e-07 0.034930 0.027944 3 3.062875
ZML2 9.970070e-07 0.014970 0.036926 3 3.051897
NAC089 9.970070e-07 0.036926 0.021956 3 3.058883
In [43]:
## Procambium
celltype = 'pro'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[43]:
pro_betweenness_centrality pro_out_centrality pro_in_centrality centrality_count centrality_sum
HAT3 0.978032 0.174538 0.023614 3 4.176184
AT4G17780 0.977618 0.079055 0.013347 3 4.070021
AT1G10610 0.833019 0.004107 0.003080 3 3.840206
LBD21 0.730800 0.002053 0.002053 3 3.734907
DEL2 0.726224 0.019507 0.018480 3 3.764212
SUVH4 0.714682 0.005133 0.044148 3 3.763964
AT2G40200 0.704067 0.204312 0.135524 3 4.043903
HSFB4 0.609961 0.053388 0.071869 3 3.735217
IAA32 0.349395 0.003080 0.022587 3 3.375062
DAR6 0.288049 0.021561 0.039014 3 3.348624
MYB10 0.156415 0.147844 0.023614 3 3.327873
AT5G64950 0.067556 0.001027 0.008214 3 3.076796
AGL13 0.038990 0.002053 0.004107 3 3.045150
AT4G00270 0.023285 0.002053 0.016427 3 3.041765
AT3G58630 0.009288 0.017454 0.025667 3 3.052409
STO 0.008625 0.247433 0.094456 3 3.350514
MBD7 0.005509 0.015400 0.007187 3 3.028096
EMB93 0.002559 0.004107 0.028747 3 3.035413
ORC1A 0.001027 0.008214 0.021561 3 3.030801
SUVH9 0.000434 0.001027 0.011294 3 3.012754
AT3G04850 0.000338 0.024641 0.072895 3 3.097874
AT4G38070 0.000231 0.001027 0.006160 3 3.007418
TRFL6 0.000150 0.042094 0.071869 3 3.114113
AT5G24320 0.000147 0.024641 0.053388 3 3.078175
bHLH115 0.000138 0.058522 0.108830 3 3.167489
MYC4 0.000132 0.019507 0.002053 3 3.021692
KELP 0.000074 0.006160 0.054415 3 3.060649
AT2G38090 0.000037 0.067762 0.025667 3 3.093466
CHR24 0.000035 0.029774 0.064682 3 3.094491
AT3G10040 0.000016 0.043121 0.053388 3 3.096525
AT5G55580 0.000006 0.015400 0.030801 3 3.046208
In [44]:
## Xylem
celltype = 'xyl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[44]:
xyl_betweenness_centrality xyl_out_centrality xyl_in_centrality centrality_count centrality_sum
DOF2 0.986211 0.728662 0.295541 3 5.010415
MYB83 0.944999 0.608917 0.039490 3 4.593406
AT4G16610 0.919459 0.224204 0.038217 3 4.181880
VND1 0.537451 0.444586 0.084076 3 4.066114
LBD31 0.492503 0.107006 0.159236 3 3.758745
SUVH5 0.458407 0.001274 0.007643 3 3.467324
HB31 0.192516 0.157962 0.003822 3 3.354299
GIF3 0.152439 0.086624 0.057325 3 3.296388
BZIP49 0.136751 0.068790 0.035669 3 3.241210
JLO 0.132577 0.160510 0.174522 3 3.467609
PIF4 0.117152 0.038217 0.049682 3 3.205050
AT3G19500 0.105939 0.002548 0.012739 3 3.121225
XND1 0.095090 0.603822 0.308280 3 4.007192
LBD18 0.064731 0.196178 0.152866 3 3.413776
MYB46 0.057648 0.672611 0.243312 3 3.973572
VND7 0.042542 0.622930 0.187261 3 3.852733
VND4 0.040787 0.528662 0.244586 3 3.814035
VND5 0.036226 0.582166 0.150318 3 3.768710
MYB85 0.032811 0.099363 0.109554 3 3.241728
AT1G68200 0.018455 0.256051 0.035669 3 3.310175
AT3G22560 0.017150 0.015287 0.057325 3 3.089762
ZHD3 0.006867 0.225478 0.126115 3 3.358459
VND6 0.001966 0.128662 0.029299 3 3.159928
CRF10 0.001423 0.016561 0.034395 3 3.052379
AT1G24040 0.001274 0.011465 0.075159 3 3.087898
HB34 0.001118 0.094268 0.052229 3 3.147615
MYB52 0.000902 0.099363 0.092994 3 3.193258
WOX14 0.000231 0.002548 0.026752 3 3.029530
AT3G49800 0.000094 0.022930 0.017834 3 3.040859
AT3G17100 0.000032 0.029299 0.012739 3 3.042071
AT1G51200 0.000019 0.024204 0.048408 3 3.072631
AT2G23780 0.000010 0.019108 0.052229 3 3.071347
GATA6 0.000006 0.005096 0.002548 3 3.007650
LUH 0.000005 0.024204 0.045860 3 3.070069
AT3G22100 0.000003 0.029299 0.014013 3 3.043315
HMGB4 0.000002 0.035669 0.066242 3 3.101912
In [45]:
## Phloem
celltype = 'phl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[45]:
phl_betweenness_centrality phl_out_centrality phl_in_centrality centrality_count centrality_sum
AT5G41380 9.915084e-01 0.447703 0.164223 3 4.603434
GATA19 9.204299e-01 0.029326 0.014663 3 3.964418
WOX2 8.677779e-01 0.045943 0.051808 3 3.965530
AT2G19260 8.246285e-01 0.027370 0.012708 3 3.864707
AT1G60700 7.961657e-01 0.007820 0.006843 3 3.810828
AT3G46070 6.607977e-01 0.006843 0.106549 3 3.774190
GYRB3 6.237716e-01 0.027370 0.003910 3 3.655052
AGL30 5.332643e-01 0.003910 0.010753 3 3.547927
AT3G12730 5.135179e-01 0.720430 0.499511 3 4.733459
REM22 3.762130e-01 0.064516 0.102639 3 3.543368
AT-HSFA5 2.491530e-01 0.012708 0.002933 3 3.264793
BPEp 1.351527e-01 0.041056 0.029326 3 3.205534
AT4G37180 1.198750e-01 0.580645 0.258065 3 3.958585
NAC057 1.191442e-01 0.460411 0.356794 3 3.936349
NAC014 9.547339e-02 0.002933 0.022483 3 3.120889
AT1G64530 7.718464e-02 0.101662 0.041056 3 3.219902
AT5G40710 7.194794e-02 0.031281 0.032258 3 3.135487
AT1G03150 5.692363e-02 0.015640 0.028348 3 3.100912
AT1G47570 5.658695e-02 0.078201 0.105572 3 3.240360
ARF17 4.700021e-02 0.021505 0.017595 3 3.086101
PRR3 4.285580e-02 0.000978 0.029326 3 3.073159
DPA 4.027045e-02 0.008798 0.017595 3 3.066663
YY1 3.544504e-02 0.004888 0.013685 3 3.054018
HSF3 1.904724e-02 0.011730 0.019550 3 3.050328
AT1G44810 1.857952e-02 0.007820 0.016618 3 3.043017
AT2G47210 1.817780e-02 0.011730 0.016618 3 3.046526
FRS9 1.559915e-02 0.011730 0.004888 3 3.032217
TRFL7 1.545759e-02 0.019550 0.009775 3 3.044783
FRS6 7.955956e-03 0.009775 0.000978 3 3.018709
ELF6 5.333303e-03 0.011730 0.005865 3 3.022929
AT2G20400 4.020063e-03 0.007820 0.006843 3 3.018683
PHL1 1.281676e-03 0.015640 0.029326 3 3.046247
AT5G65130 9.918642e-04 0.019550 0.048876 3 3.069418
SDG40 9.775171e-04 0.004888 0.058651 3 3.064516
ZAP1 9.182157e-04 0.069404 0.033236 3 3.103558
ZML1 8.627401e-04 0.009775 0.009775 3 3.020413
AT4G21060 8.579578e-04 0.013685 0.024438 3 3.038981
OXS2 8.493495e-04 0.023460 0.032258 3 3.056568
bHLH104 4.638902e-04 0.039101 0.048876 3 3.088440
NF-YC3 1.922514e-04 0.080156 0.062561 3 3.142910
DEL1 7.364855e-05 0.009775 0.073314 3 3.083163
MNP 7.077913e-05 0.004888 0.000978 3 3.005936
RSZ22 4.973668e-05 0.025415 0.039101 3 3.064566
AT2G24650 4.399783e-05 0.015640 0.015640 3 3.031325
AT1G48040 3.538956e-05 0.031281 0.005865 3 3.037181
PEX10 3.347661e-05 0.026393 0.017595 3 3.044022
FVE 2.965071e-05 0.016618 0.020528 3 3.037175
AT1G68070 2.295539e-05 0.024438 0.010753 3 3.035214
AT1G61730 1.147770e-05 0.055718 0.048876 3 3.104606
RGD3 4.782373e-06 0.026393 0.020528 3 3.046926
AT4G31420 3.825899e-06 0.034213 0.010753 3 3.044970
CPUORF7 3.825899e-06 0.047898 0.020528 3 3.068430
AIP3 2.869424e-06 0.022483 0.040078 3 3.062564
DA1 1.912949e-06 0.042033 0.013685 3 3.055720
GIF2 1.912949e-06 0.022483 0.040078 3 3.062563
GATA26 1.912949e-06 0.020528 0.011730 3 3.032260
ERF3 9.564747e-07 0.044966 0.023460 3 3.068427
In [46]:
tf_spec.to_csv('scRNAseq_TF_GRN_centrality_t3-t5_zscore3.csv', index=True)
In [47]:
tf_spec
Out[47]:
tf_occurance atri_degree_centrality atri_out_centrality atri_in_centrality atri_betweenness_centrality atri_closeness_centrality atri_eigenvector_centrality tri_degree_centrality tri_out_centrality tri_in_centrality ... phl_in_centrality phl_betweenness_centrality phl_closeness_centrality phl_eigenvector_centrality col_degree_centrality col_out_centrality col_in_centrality col_betweenness_centrality col_closeness_centrality col_eigenvector_centrality
UPB1 7.0 0.538931 0.512977 0.025954 0.033242 0.000222 0.160249 0.166667 0.151163 0.015504 ... 0.098729 0.011077 0.000392 0.055477 0.005229 0.001307 0.003922 0.000000 0.000194 0.003018
ARF9 7.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.032258 0.100595 0.000290 0.026006 0.053595 0.001307 0.052288 0.000002 0.000318 0.017174
BZIP61 7.0 0.105344 0.024427 0.080916 0.001450 0.000189 0.069691 0.094961 0.003876 0.091085 ... 0.111437 0.003737 0.000397 0.049084 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
AT4G30410 6.0 0.453435 0.432061 0.021374 0.018398 0.000219 0.142302 0.598837 0.472868 0.125969 ... 0.045943 0.000000 0.000340 0.030540 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
HAT22 6.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.050831 0.506691 0.000288 0.028702 0.103268 0.079739 0.023529 0.000031 0.000257 0.030739
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
AT3G06220 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.003910 0.000000 0.000190 0.003155 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
IAA33 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.013685 0.000000 0.000230 0.009545 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
AT1G62010 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.017595 0.000000 0.000243 0.011790 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
RAD9 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.006843 0.000000 0.000156 0.005332 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
AT3G18960 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.001955 0.000000 0.000164 0.001868 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

1466 rows × 61 columns

In [ ]: